Add comprehensive example demonstrating grid search for hyperparameter tuning with regularized regression models.

Background:
Grid search is a fundamental technique for hyperparameter optimization, but we currently lack an example showing how to use our grid_search_alpha() function for tuning regularization parameters.

Scope:
Create example demonstrating:

1. Grid search basics with cross-validation
2. Tuning alpha for Ridge regression
3. Tuning alpha for Lasso regression
4. Tuning alpha and l1_ratio for ElasticNet
5. Interpreting GridSearchResult (best params, scores, indices)
6. Comparing optimal vs. default hyperparameters

Deliverables:

• Create examples/grid_search_tuning.rs
• Add book chapter: book/src/examples/grid-search-tuning.md
• Update SUMMARY.md
• Show performance improvement from tuning
• Demonstrate cross-validation integration
• Include visualization of alpha vs. score curves

Use Case:
Show practical workflow:

1. Define parameter grid
2. Run grid search with K-Fold CV
3. Identify best parameters
4. Retrain with optimal params
5. Evaluate on test set

Labels: documentation, enhancement, model-selection